Modelling of Autonomous Search and Rescue Missions by Interval-Valued Neutrosophic WASPAS Framework

Abstract

The application of autonomous robots in search and rescue missions represents a complex task which requires a robot to make robust decisions in unknown and dangerous environments. However, imprecise robot movements and small measurement errors obtained by robot sensors can have an impact on the autonomous environment exploration quality, and therefore, should be addressed while designing search and rescue (SAR) robots. In this paper, a novel frontier evaluation strategy is proposed, that address technical, economic, social, and environmental factors of the sustainable environment exploration process, and a new extension of the weighted aggregated sum product assessment (WASPAS) method, modelled under interval-valued neutrosophic sets (IVNS), is introduced for autonomous mobile robots. The general-purpose Pioneer 3-AT robot platform is applied in simulated search and rescue missions, and the conducted experimental assessment shows the proposed method efficiency in commercial and public-type building exploration. By addressing the estimated measurement errors in the initial data obtained by the robot sensors, the proposed decision-making framework provides additional reliability for comparing and ranking candidate frontiers. The interval-valued multi-criteria decision-making method combined with the proposed frontier evaluation strategy enables the robot to exhaustively explore and map smaller SAR mission environments as well as ensure robot safety and efficient energy consumption in relatively larger public-type building environments.

A Novel Environment Exploration Strategy by m-generalised q-neutrosophic WASPAS

“…The criterion is minimised to prioritise frontiers located close to the robot to reduce backtracking behaviour; -2 C , the expected information gain is considered to be equal to the frontier length and is maximised to direct the robot to frontiers which are assumed to border wide open spaces; -3 C , the estimated time needed to reach the frontier is applied to prioritise the frontiers close to the robot and reachable by short and straight paths. To estimate the criterion value, the methodology defined by Basilico & Amigoni (2011), which was also applied in our previous robot setup (Semenas & Bausys, 2020), is applied in this case. The constant average robot movement and rotation velocities of 0.4 m/s and 0.5 rad/s are used in this research; -4 C , the distance to the robot control station is measured as the Euclidean distance between the assessed frontier and the robot starting position.…”

“…The autonomous robot deployed in a Gazebo simulator is controlled by utilising the robot operating system (ROS) and using a similar navigation framework and sensor setup as the one introduced in our previous research (Semenas & Bausys, 2020). The robot utilises the frontierbased environment exploration approach.…”

Participant Trustworthiness Analysis in the Game-Based Urban Planning Processes by Promethee-MGQNN Approach

“…Multicriteria decision making approaches (MCDM) are widely exploited in different contexts. Recently they were applied to assess construction labours' safety level (Mohandes et al, 2020), to rank observation locations for autonomous robot environment exploration tasks (Semenas & Bausys, 2020), to reflect the psychometric features of the Visual Analogue Scales (Lescauskiene et al, 2020), to increase the accuracy of the checklistsbased quantitative heuristic evaluation (Zavadskas et al, 2021) or to detect edges in satellite images (Bausys et al, 2020). However, there are only a few examples of the MCDM applications in the growing video game industry, and most of them are dedicated to rank the credibility of the e-sport players (Pradhan & Abdourazakou, 2020;Urbaniak et al, 2020).…”

VASMA Weighting: Survey-Based Criteria Weighting Methodology that Combines ENTROPY and WASPAS-SVNS to Reflect the Psychometric Features of the VAS Scales

“…Criteria weighting is an integral part of the multicriteria decision making (MCDM) models, that are widely applied in economics [1], service quality [2], talent identification process [3], robotics [4], healthcare [5], social studies [6], and other areas. Differences in the preference elicitations methodologies, transparency of the evaluation process, diversity of the opinions, and the competence of the decision-makers (DM) are the important factors affecting the final values of the criteria weights [7].…”